Detecting Deepfake Talking Heads from Facial Biometric Anomalies
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Abstract (arXiv)
The combination of highly realistic voice cloning, along with visually compelling avatar, face-swap, or lip-sync deepfake video generation, makes it relatively easy to create a video of anyone saying anything. Today, such deepfake impersonations are often used to power frauds, scams, and political disinformation. We propose a novel forensic machine learning technique for the detection of deepfake video impersonations that leverages unnatural patterns in facial biometrics. We evaluate this technique across a large dataset of deepfake techniques and impersonations, as well as assess its reliability to video laundering and its generalization to previously unseen video deepfake generators.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 11 Jul 2025.
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14 of 39 references.
References · 14
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 632
- 2019Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensicscited by 575
- 2019Face X-ray for More General Face Forgery Detectioncited by 470
- 2018MesoNet: a Compact Facial Video Forgery Detection Networkcited by 409
- 2021Multi-attentional Deepfake Detectioncited by 341
- 2019FSGAN: Subject Agnostic Face Swapping and Reenactmentcited by 320
- 2018Exposing Deep Fakes Using Inconsistent Head Posescited by 297
- 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 163
- 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
- 2022Audio-Visual Person-of-Interest DeepFake Detectioncited by 43
- 2024Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakescited by 32
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Citing papers per year: 2026: 1.
1 paper.